# octave-se-resnet-50-0.125¶

## Use Case and High-Level Description¶

The octave-se-resnet-50-0.125 model is a modification of se-resnet-50 from this paper with octave convolutions from Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution with alpha=0.125. As origin, it’s designed to perform image classification. For details about family of octave convolution models, check out the repository.

The model input is a blob that consists of a single image of 1, 3, 224, 224 in RGB order. The RGB mean values need to be subtracted as follows: [124, 117, 104] before passing the image blob into the network. In addition, values must be divided by 0.0167.

The model output for octave-se-resnet-50-0.125 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Metric

Value

Type

Classification

GFLOPs

7.246

MParams

28.082

Source framework

MXNet*

Metric

Value

Top 1

78.706%

Top 5

94.09%

## Input¶

### Original model¶

Image, name - data, shape - 1, 3, 224, 224, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is RGB. Mean values - [124, 117, 104], scale value - 59.880239521

### Converted model¶

Image, name - data, shape - 1, 3, 224, 224, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR

## Output¶

### Original model¶

Object classifier according to ImageNet classes, name - prob, shape - 1, 1000, output data format is B, C, where:

• B - batch size

• C - predicted probabilities for each class in [0, 1] range

### Converted model¶

Object classifier according to ImageNet classes, name - prob, shape - 1, 1000, output data format is B, C, where:

• B - batch size

• C - predicted probabilities for each class in [0, 1] range

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>